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Exponential Stabilization of Neutral-Type Neural Networks with Mixed Interval Time-Varying Delays by Intermittent Control: A CCL Approach

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Abstract

For a class of neutral-type neural networks with discrete and distributed delays, the problem of exponential stabilization is studied in this paper by using periodically intermittent control. By introducing an appropriate Lyapunov–Krasovskii functional and using matrix inequality techniques to deal with its derivative, exponential stabilization criteria are presented in the form of nonlinear matrix inequalities. In order to solve the nonlinear problem, a cone complementarity liberalization (CCL) algorithm is proposed. In addition, numerical examples are provided to illustrate the applicability of the proposed approach.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 11371006, the fund of Heilongjiang Province Innovation Team Support Plan under Grant No. 2012TD007, the fund of Heilongjiang University Innovation Team Support Plan under Grant No. Hdtd2010–03, the Heilongjiang Province Innovation Fund for Graduates under Grant No. YJSCX2012-315HLJ, and the Fund of Key Laboratory of Electronics Engineering, College of Heilongjiang Province, (Heilongjiang University), P.R. China.

The authors thank the anonymous referees for their helpful comments and suggestions which improved greatly this note.

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  1. School of Mathematical Science, Heilongjiang University, Harbin, 150080, China

    Guodong Zhang, Xue Lin & Xian Zhang

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  1. Guodong Zhang
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  2. Xue Lin
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  3. Xian Zhang
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Correspondence to Xian Zhang.

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Zhang, G., Lin, X. & Zhang, X. Exponential Stabilization of Neutral-Type Neural Networks with Mixed Interval Time-Varying Delays by Intermittent Control: A CCL Approach. Circuits Syst Signal Process 33, 371–391 (2014). https://doi.org/10.1007/s00034-013-9651-y

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  • DOI: https://doi.org/10.1007/s00034-013-9651-y

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